論文 - 大槻　知明

Dynamic Antenna Control for HAPS Using Mean Field Reinforcement Learning in Multi-Cell Configuration

Yang S., Bouazizi M., Ohtsuki T.

IEEE International Conference on Communications 2023-May   5670 - 5676 2023年

ISSN  15503607

　概要を見る

In this paper, we propose a Mean Field reinforcement learning (MFRL) method for dynamic antenna control in High-Altitude-Platform-Station (HAPS) communication system with Multi-Cell Configuration. HAPS works at stratospheric altitudes of about 20 km to provide an ultra-wide coverage area. However, the wind pressure caused HAPS movement leads to the degradation of users' throughput. Considering the multi-antenna arrays in the HAPS, to find the optimal antenna parameters of all antenna arrays for reducing the number of low-throughput users, we formulate the antenna control problem into stochastic game equilibrium. Usually, solving the stochastic to find the equilibrium needs very high computation complexity to calculate the transition probability for getting the Q-value under a certain state and action. Therefore, we use the reinforcement learning (RL) named Deep Q-Network (DQN) to learn the transition probability and predict the Q-value according to the reward fed backed from the environment. Besides, we employ the Mean field Game theory in conjunction with RL during the training phase of DQN to reduce the complexity of the interactions among agents. To evaluate the proposed method, we compare the proposed method with a genetic algorithm (GA) named Particle Swarm Optimization (PSO), Q-learning, Fuzzy Q-learning, and conventional DQN under four realistic user distribution scenarios. The simulation results show that the proposed method achieves comparable throughput performance with a high convergence rate.

• Access to Document (DOI)

Depression Detection via User Behavior and Tweets

Ji N., Bouazizi M., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    1985 - 1990 2023年

ISSN  23340983

　概要を見る

Depression is a common mental illness and the second leading cause of disability worldwide. Traditional depression diagnosis requires communication with patients and subjective cooperation of patients, which consumes a lot of manpower, material resources, and time costs. With the accumulation of user data in social media and the development of natural language processing, computer-aided diagnosis is realized, better and objective analysis is provided, and a new idea for the diagnosis of depression is provided. We propose a multimodal model based on EmoBERTa and Transformer and a text preprocessing method for a specific pre-trained model and application context. We use user behavior information and past tweets to detect whether a user has depression. Our model achieves state-of-the-art performance on a publicly available multimodal Twitter dataset.

• Access to Document (DOI)

Deep Reinforcement Learning Aided Online Trajectory Optimization of Cellular-Connected UAVs with Offline Map Reconstruction

Hao Q., Zhao H., Huang H., Gui G., Ohtsuki T., Adachi F.

IEEE Vehicular Technology Conference 2023-June 2023年

ISSN  15502252

　概要を見る

To reduce the outage of the connection between unmanned aerial vehicles (UAVs) and cellular networks in complex real-time channel state, and reduce the energy consumption of UAV during flight mission, an online trajectory optimization scheme of UAV based on outage probability knowledge map reconstruction is proposed. The outage probability knowledge map is a database that simulates the connection between UAV and the cellular network during real hovers. The UAV first samples sparsely from the target area and calculates the outage probability of the sampling point, and then uses the Kriging algorithm to reconstruct the outage probability knowledge map. Based on the reconstructed outage probability knowledge map, with the goal of minimizing the energy consumption of UAV task execution, the UAV trajectory optimization problem is established, and a trajectory optimization algorithm based on deep reinforcement learning (DRL) is proposed to solve it. Numerical results show that the proposed online trajectory optimization scheme based on outage probability knowledge map can obtain great returns in terms of maintaining connectivity, reducing task completion time and energy consumption.

• Access to Document (DOI)

Automatic Modulation Classification Based on Decentralized Learning and Model Averaging

Xu Y., Ma M., Fu X., Wang Y., Gui G., Ohtsuki T.

IEEE INFOCOM 2023 Conference on Computer Communications Workshops INFOCOM Wkshps 2023 2023年

　概要を見る

Automatic modulation classification (AMC) is a promising technology to identify the modulation mode of the received signal in non-cooperative communication scenarios. Recently, benefitting from the outstanding classification performance of deep learning (DL), various deep neural networks (DNNs) have been introduced into AMC methods. Most current AMC methods are based on local framework (LocalAMC) where there is only one device, or centralized framework (CentAMC) where multiple local devices (LDs) upload their data to the only one central server (CS). LocalAMC may not get ideal results due to insufficient data and finite computational power. CentAMC carries a significant risk of privacy leakage and the final data for training model in CS is quite massive. In this paper, we propose an AMC method based on decentralized learning with residual network (ResNet). Simulation results show that ResNet-based decentralized AMC (DecentAMC) method achieves similar classification performance to CentAMC while improving training efficiency and protecting data privacy.

• Access to Document (DOI)

Arterial Blood Pressure Waveform Estimation from Photoplethysmogram Under Inter-Subject Paradigm Using Subject-Distinguishable Dataset by U-Net and Domain Adversarial Training

Yoshizawa R., Yamamoto K., Ohtsuki T.

IEEE International Conference on Communications 2023-May   3401 - 3406 2023年

ISSN  15503607

　概要を見る

Blood pressure (BP) estimation methods using photoplethysmogram (PPG) based on deep learning models have been actively studied. These methods are also the basis of non-contact BP estimation methods using a camera or a Doppler radar. However, most previous studies are under data leakage, where subjects are not separated between training and test data. In this paper, we propose a method for BP estimation from PPG under the condition that subjects are separated between training and test data (inter-subject paradigm) using a subject-distinguishable large public dataset. Our BP estimation method estimates an 8-second BP waveform called arterial BP (ABP) from an 8-second PPG segment using U-Net. From the estimated ABP, systolic BP (SBP), diastolic BP (DBP), and mean BP (MBP) are calculated, which are the discrete single values for the 8-second ABP. In addition, we apply domain adversarial training, which facilitates the BP estimation model to extract subject-invariant features to improve the BP estimation accuracy under the inter-subject paradigm. Moreover, we use a considerably larger amount of training data than the previous studies and evaluate our model with smaller model sizes for better generalizability. Our experimental results showed that our method can estimate BP with moderate accuracy under the inter-subject paradigm, particularly MBP. The mean absolute errors for the estimated SBP, DBP, MBP, and ABP were 15.21, 7.12, 8.20, and 10.14 mmHg, respectively. The Pearson's correlation coefficients between the true and estimated values were 0.49, 0.39, 0.54, and 0.84, respectively.

• Access to Document (DOI)

An LSTM-Based Approach for Fall Detection Using Accelerometer-Collected Data

Uotani Y., Yamamoto K., Ye C., Bouazizi M., Ohtsuki T.

Proceedings 2023 28th Asia Pacific Conference on Communications Apcc 2023    250 - 255 2023年

　概要を見る

Over the past few years, there has been a significant rise in the number of fall accidents occurring among elderly individuals, a problem that has been accentuated with to the aging population. Researchers and developers have focused their efforts on investigating and creating various fall detection methods that utilize an accelerometer. However, conventional fall detection methods typically target specific positions where accelerometers are placed. In addition, they suffer from low accuracy which can be attributed to the fact that the classification algorithms commonly employed, such as the support vector machine (SVM) and the random forest (RF), are not specialized in making predictions based on time series data. In this paper, we propose the fall detection method based on a long short-term memory (LSTM) neural network, using an accelerometer. In the proposed method, four kinds of possession positions are set: (i) in hand, (ii) inside a chest pocket, (iii) inside a waist pocket, and (iv) in a bag. The acceleration data collected are classified using the LSTM classifies into one of four classes: (i) standing, (ii) walking, (iii) falling, and (iv) lying down. The results of the multi-class classification are further reclassified into two classes, i.e., fall and non-fall. The experimental results demonstrate that our approach outperforms the conventional methods in terms of fall detection accuracy.

• Access to Document (DOI)

Adversarial Learning Aided Malicious Network Traffic Detection Based on EDE Framework

Zhang L., Wang Y., Ohtsuki T., Adebisi B., Wu H.C., Gui G.

2023 IEEE Cic International Conference on Communications in China Iccc 2023 2023年

　概要を見る

With the increasing number of new malicious software and network attacks, anomalous network traffic detection technology needs to continuously identify unknown attacks in order to adapt to complex network environments. In this paper, we propose a novel adversarial learning aided malicious network traffic detection (AMD) method based on encoder-decoder-encoder (EDE) framework. The proposed AMD method detects unknown anomalies in a single type of semi-supervised form and converts network traffic data into pictures for later processing. First, an anomalous network traffic detection model consisting of two encoders, a decoder and a discriminator is introduced. Second, by using the visibility of normal samples and the unrecognizability of unknown anomaly samples, the difference between the two samples is enlarged to facilitate effective sample detection. Finally, experiments show that the proposed model detects anomalies in targeted space and is robust to complex and noisy network environments. At the same time, compared to traditional machine learning methods, AMD's shows performance improvement of up to 40%.

• Access to Document (DOI)

A Robust Multi-Frame mmWave Radar Point Cloud-Based Human Skeleton Estimation Approach with Point Cloud Reliability Assessment

Shi X., Ohtsuki T.

Proceedings of IEEE Sensors 2023年

ISSN  19300395

　概要を見る

Millimeter-Wave (mmWave) radar-based skeleton estimation has gained significant attention in the field of human motion analysis and sensing. It offers distinct advantages over RGB camera-based, depth camera-based, and inertial sensor-based approaches. It operates independently of lighting conditions. Additionally, it overcomes limitations of depth camera-based methods such as reflections and occlusions, and provides precise real-time tracking, enhancing its overall performance. However, existing human skeleton estimation methods utilizing point cloud data mostly rely on single-frame inputs or incorporate voxelization, which introduces drawbacks. This paper proposes a novel Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM)-based model for multi-frame point cloud data without voxelization. Furthermore, an Long Short-Term Memory (LSTM)-based neural network is introduced to assess point cloud reliability, enhancing robustness. Experimental results demonstrate improved accuracy and robustness in human skeleton estimation compared with the conventional single frame-based methods.

• Access to Document (DOI)

A Novel Approach for Activity, Fall and Gait Detection Using Multiple 2D LiDARs

Bouazizi M., Feghoul K., Lorite A., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    1997 - 2002 2023年

ISSN  23340983

　概要を見る

A key concept in health monitoring systems for elderly people is the continuous and non-intrusive detection of their activities to identify when hazardous events such as sudden falling occur/are about to occur. The existence of obstacles in the environment largely limits the detection performance of existing approaches of activity detection relying on non-contact sensors. A simple, yet effective, approach to address this issue is the use of multiple sensors which collaborate with one another. In this paper, we propose an approach that relies on 2D Light Detection and Ranging (LiDAR) technology for activity detection. We employ multiple 2D LiDARs placed at different locations in a single room with difference obstacles (e.g., furniture) and working in coordination to construct a fuller representation of the activities being performed. Our approach transforms the concatenation of the different LiDAR data into a more comprehensible data format (i.e., images). The generated images are then processed using a Convolutional LSTM Neural Network to perform the classification. For 3 different tasks, namely activity detection, fall detection, and unsteady gate detection, our proposed approach reaches an accuracy equal to 96.10%, 99.13% and 93.13%, respectively.

• Access to Document (DOI)

A GAN-Based Approach for ECG Reconstruction from Doppler Sensor Signals

Bouazizi M., Yu D., Feghoul K., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    3867 - 3872 2023年

ISSN  23340983

　概要を見る

An Electrocardiograms (ECG) is a recording of the heart's electrical activity. It can help detect problems with one's heart rate or heart rhythm. Traditional methods to measure and collect ECG are not practical for daily use due to their invasive nature and inaccessibility. Thus, a less intrusive and more accessible method is needed. In this paper, we propose a method that uses attention-based Wasserstein Generative Adversarial Networks-Gradient Penalty (aWGAN-GP) to reconstruct ECG signals from ones collected using a Doppler sensor. GANs can capture pertinent features in the Doppler signals to enable such reconstruction. Our approach uses the integrated spectrum of the Doppler signal to identify R-peaks, which are then employed to train the aWGAN-GP to reconstruct the ECG signal. We evaluated this method on 19 healthy subjects and obtained a correlation coefficient of 0.88 between the reconstructed and actual ECG signals, outperforming the conventional CNN-based method which reached only 0.35. Our results demonstrate that it is possible to reconstruct an ECG signal from a heartbeat signal collected via a Doppler sensor using aWGAN-GP.

• Access to Document (DOI)

A Few-Shot Malware Traffic Classification Method Using Knowledge Transfer Learning

Qin M., Zhang X., Wang Y., Ohtsuki T., Sari H., Gui G.

International Conference on Communication Technology Proceedings ICCT    15 - 19 2023年

ISSN  25767844

　概要を見る

Malware traffic classification (MTC) has become one of the important tasks to protect network and system security. However, it is a difficult task to obtain large-scale labeled malware traffic data in practice due to the wide variety and ever-changing malware. Existing MTC techniques require a large number of samples for training, the classification performance is poor when the training samples are few. Therefore, classification techniques based on few-shot learning (FSL) have attracted much attention in MTC. In this paper, we propose a few-shot MTC method based on knowledge transfer learning. Specifically, generic malware feature representation can be learned by pre-training the deep learning model on source tasks. Then, the parameters of the pre-trained model are used as initial parameters to fine-tune on the target task to adapt to feature distribution of the target task. The simulation results show that the few-shot MTC method based on knowledge transfer learning proposed in this paper has a 20-shot average accuracy of 95.23% when using GoogLeNet network as the backbone network, compared with CNN, VGG, and ResNet models, GoogLeNet has the best performance.

• Access to Document (DOI)

Multilayer Attention Mechanism for Change Detection in SAR Image Spatial-Frequency Domain

Ma L., Wang L., Zhao C., Jiahui E., Ohtsuki T.

Proceedings International Conference on Image Processing Icip    2110 - 2114 2023年

ISSN  15224880

　概要を見る

Change detection based on synthetic aperture radar (SAR) images is a challenging task in the field of remote sensing image analysis due to the influence of noise and the lack of labeled data. In this paper, we propose a new unsupervised change detection algorithm based on deep learning, which explores the spatial and frequency domain features of SAR images in parallel to improve detection performance. Our proposed method first obtains pseudo-labels by clustering and then combines them with neural networks for unsupervised detection. To reduce the impact of noise and improve sensitivity to changes, we integrate an attention mechanism (AM) into the network. We also use complementary features to integrate the spatial and frequency domain features. These complementary features include a multi-regional feature weighted by channel-spatial AM and a deep feature filtered out by a gated linear unit (GLU). Experimental results demonstrate that the proposed method improves the detection accuracy.

• Access to Document (DOI)

Semi-Supervised Malware Traffic Classification Via Consistency Regularization and Pseudo-Labeling

Jiao Q., Zhang X., Zhang W., Miao F., Ohtsuki T., Adebisi B., Wang Y., Gui G.

International Conference on Communication Technology Proceedings ICCT    1008 - 1012 2023年

ISSN  25767844

　概要を見る

Malware traffic classification (MTC) technology plays a crucial role in cyber security by serving as the first step of network intrusion detection system. Recently, deep learning has been successfully introduced into the field of network intrusion detection. However, its performance relies heavily on the quantity and quality of labeled samples, which are often difficult to obtain in real-world applications. To address these challenges, we propose semi-supervised MTC method based on consistency regularization and pseudo-labeling. This method utilizes unlabeled malware traffic data to improve the classification performance of the model. Specifically, we train the model using labeled malware traffic data. Then, weak data augmentation is performed on unlabeled traffic data and pseudo labels are generated through the model. Finally, the model predicts unlabeled data after strong data augmentation and compares it with pseudo labels. Model parameters are updated using losses from labeled and unlabeled samples. Experimental results demonstrate that our proposed method outperforms other classical classification approaches, including ladder networks, virtual adversarial training (VAT), convolutional neural network (CNN), and support vector machine (SVM), when the ratios of labeled samples range from 1% to 5%.

• Access to Document (DOI)

Radio Frequency Signal Dataset Generation Based on LTE System and Variable Channels

Zhang S., Zhang Y., Zhang X., Peng Y., Sun J., Gui G., Ohtsuki T.

IEEE INFOCOM 2023 Conference on Computer Communications Workshops INFOCOM Wkshps 2023 2023年

　概要を見る

Deep learning-based radio frequency fingerprinting (RFF) identification has the potential to enhance the security performance of the physical layer. In recent years, a number of RFF datasets have been proposed to meet the large-scale data requirements for deep learning. However, these datasets are collected from similar channel environments and only contain receiver data. This paper employs different software radio peripherals to generate radio signals. Hence, it is able to adjust the signal's parameters, such as frequency band, modulation style, antenna gain, etc. In this paper, we propose a radio frequency signal dataset based on LTE system and variable channels to more properly characterize the generated signals in the real world. We collect signals at transmitters and receivers to construct the RFF dataset. Moreover, we confirm the dataset's dependability using various machine learning and deep learning methods. The dataset and reproducible code of this paper can be downloaded from GitHub11GitHub link: https://github.com/njuptzsp/XSRPdataset.

• Access to Document (DOI)

Radio Frequency Fingerprint Identification Based on Length-Robust Representation and Incremental Learning

Shi F., Feng Z., Miao F., Fu X., Adebisi B., Ohtsuki T., Sari H., Gui G.

International Conference on Communication Technology Proceedings ICCT    236 - 240 2023年

ISSN  25767844

　概要を見る

Radio frequency fingerprinting identification (RFFI) is a technology that uses signal processing techniques to extract the characteristics of collected wireless signals to enable the individual identification of devices. Over the past few years, deep learning (DL) has made great achievements in the field of signal identification, and the research on the RFFI methods has made great progress. To address the issue of fixed-size input data required by many neural networks, such as convolutional neural network (CNN) and multilayer perceptron (MLP), this paper proposes a transformation method that converts variable-length signals into contour stella constellation (CSC). In addition, in the most DL-based the RFFI methods, a global analysis of the entire radio frequency signal is required, which demands a large amount of calculation and is easily affected by changes in the propagation environment. We propose an incremental learning (IL)-based the RFFI approach, which enables dynamic updating of the model and improves its identification and generalization capabilities. Experimental results show that our proposed method shows excellent performance in processing data streams in real time and the identification accuracy finally can reach 89.14%. The codes of this paper can be downloaded from https://github.com/sfuaena/CSC-KD.

• Access to Document (DOI)

Non-Contact Blood Pressure Estimation Using Accurate Cardiac Movements Extracted by Hidden Semi-Markov Model

Wang S., Boazizi M., Ohtsuki T.

IEEE International Conference on Communications 2023-May   5508 - 5514 2023年

ISSN  15503607

　概要を見る

This paper presents a radar-based, non-contact Blood Pressure (BP) estimation model based on accurate detection of cardiac activities, which enables BP monitoring to be performed in a touch-free and continuous manner. Cardiac movements have been regarded as essential factors for BP estimation. However, accurately obtaining these movements by a radar system remains a challenging problem because these movements are too inconspicuous and could be easily hindered by respiration and random noise. In this paper, we propose a method that mainly focuses on cardiac feature extraction in radar-based BP monitoring. First, we employ an integrated-spectrum waveform. It is derived from short-time Fourier transform (STFT) and is capable of recording and preserving minor cardiac activities. Compared with the pulse-wave signals used in previous works, the integrated-spectrum focuses on energy changes introduced by short and high-frequency vibrations. It can eliminate the interference of respiration and random noise, and cardiac contractile movement can be reserved accurately. Second, we propose a cardiac features estimation method in which a hidden semi-Markov model (HSMM) is applied to the integrated-spectrum for feature extraction. Compared to the pulse wave signal, the Root-Mean-Square Error (RMSE) of the estimated interbeat intervals (IBI), Systolic time, and Diastolic time is reduced by 44.2%, 73.3%, and 76.7% respectively. The estimated accurate cardiac features are further used as inputs for a Random Forest model for BP prediction. Though previous work required the subject to hold one's breath, we achieved a comparable prediction accuracy even when our subject is breathing normally. The Diastolic BP (DBP) error of our model is 4.27± 5.84 mmHg (Mean Absolute Difference ± Standard Deviation), and the Systolic BP (SBP) error is 6.63± 8.95 mmHg.

• Access to Document (DOI)

NASEI: Neural Architecture Search-Based Specific Emitter Identification Method

Huang Y., Zhang X., Wang Y., Jiao D., Gui G., Ohtsuki T.

IEEE Vehicular Technology Conference 2023-June 2023年

ISSN  15502252

　概要を見る

Specific emitter identification (SEI) extracts the fingerprint characteristics of emitters according to the subtle differences of transmitted signals, to distinguish different emitter individuals and prevent unauthorized network access. Deep learning (DL) based SEI methods have been proposed to achieve a good identification performance in recent years. However, these methods highly rely on expert experience to design network structures. These hand-designed fixed network structures lack flexibility, which often leads to insufficient model generalization. Neural architecture search (NAS) can be seen as a subdomain of automatic machine learning (AutoML), which can automatically adjust network structure and parameters according to a specific task. In this paper, we propose a neural architecture search-based SEI method, which can achieve an efficient search of the architecture with the use of a gradient descent algorithm. Experimental results show that the proposed NASEI method both improves the accuracy and reduces the parameter quantity when compared with state-of-the-art methods. Code available at https://github.com/huangyuxuan11/NASEI.git.

• Access to Document (DOI)

Edge Device Identification Based on Federated Learning and Network Traffic Feature Engineering

He Z., Yin J., Wang Y., Gui G., Adebisi B., Ohtsuki T., Gacanin H., Sari H.

IEEE Transactions on Cognitive Communications and Networking （IEEE Transactions on Cognitive Communications and Networking）  8 （ 4 ） 1898 - 1909 2022年12月

　概要を見る

With the ubiquitous deployment and applications of Internet of Things (IoT), security issues pose a critical challenge to IoT devices. External attackers often utilize vulnerable IoT devices to invade the target's internal network and then further cause a security threat to the whole network. To prevent such attacks, it is necessary to develop a security mechanism to control the access of suspicious IoT devices and manage the internal devices. In recent years, deep learning (DL) algorithm has been widely used in the field of edge device identification (EDI), and has made great achievements. However, these previous methods are essentially centralized learning-based EDI (CentEDI) that trains all data together, which can not guarantee data security and not conducive to deployment on edge devices. To address this problem, we introduce a federated learning-based EDI (FedeEDI) method via network traffic to automatically identify edge devices connected to the whole network. Experimental results show that the training efficiency of our proposed FedeEDI method is much higher than that of the CentEDI method, although its classification accuracy is slightly reduced. In contrast to the CentEDI method, the proposed FedeEDI method has two main advantages: faster training speed and safer training process.

• Access to Document (DOI)

Make Smart Decisions Faster: Deciding D2D Resource Allocation via Stackelberg Game Guided Multi-Agent Deep Reinforcement Learning

Shi D., Li L., Ohtsuki T., Pan M., Han Z., Poor H.V.

IEEE Transactions on Mobile Computing （IEEE Transactions on Mobile Computing）  21 （ 12 ） 4426 - 4438 2022年12月

ISSN  15361233

　概要を見る

Device-to-Device (D2D) communication enabling direct data transmission between two mobile users has emerged as a vital component for 5G cellular networks to improve spectrum utilization and enhance system capacity. A critical issue for realizing these benefits in D2D-enabled networks is to properly allocate radio resources while coordinating the co-channel interference in a time-varying communication environment. In this paper, we propose a Stackelberg game (SG) guided multi-agent deep reinforcement learning (MADRL) approach, which allows D2D users to make smart power control and channel allocation decisions in a distributed manner. In particular, we define a crucial Stackelberg Q-value (ST-Q) to guide the learning direction, which can be calculated based on the equilibrium achieved in the Stackelberg game. With the guidance of the Stackelberg equilibrium, our approach converges faster with fewer iterations than the general MADRL method and thereby exhibits better performance in handling the network dynamics. After the initial training, each agent can infer timely D2D resource allocation strategies with distributed execution. Extensive simulations are conducted to validate the efficacy of our proposed scheme in developing timely resource allocation strategies. The results also show that our method outperforms the general MADRL based approach in terms of the average utility, channel capacity, and training time.

• Access to Document (DOI)

A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar

Endo K., Yamamoto K., Ohtsuki T.

Sensors （Sensors）  22 （ 23 ）  2022年12月

ISSN  14248220

　概要を見る

A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power that exceeds a threshold using an algorithm, such as Constant False Alarm Rate (CFAR). However, noise and multipath components often exist over the range-angle map, which could produce false alarms for an undesired location depending on the threshold setting. In other words, the threshold setting is sensitive in noisy range-angle maps. Therefore, if the noise is reduced, the threshold can be easily set to reduce the number of false alarms. In this paper, we propose a method that improves the CFAR threshold tolerance by denoising a range-angle map using Deep Image Prior (DIP). DIP is an unsupervised deep-learning technique that enables image denoising. In the proposed method, DIP is applied to the range-angle map calculated by the Curve-Length (CL) method, and then the object location is detected over the denoised range-angle map based on Cell-Averaging CFAR (CA-CFAR), which is a typical threshold setting algorithm. Through the experiments to estimate human locations in indoor environments, we confirmed that the proposed method with DIP reduced the number of false alarms and estimated the human location accurately while improving the tolerance of the threshold setting, compared to the method without DIP.

• Access to Document (DOI)